Advanced Curve-speed Warning System Using an In ...

Advanced Curve-speed Warning System Using an In-Vehicle Head-Up Display Xiao Qin, Ph.D., P.E.* Associate Professor Department of Civil and Environmental Engineering South Dakota State University CEH 148, Box 2219, Brookings, SD, 57007 Phone: (605) 688-6355 Fax: (605) 688-6476 Email:[email protected] Shaohu Zhang Research Assistant Department of Civil & Environmental Engineering South Dakota State University Brookings, SD, 57006 Phone: (605)651-2072 Email: [email protected] Wei Wang, Ph.D. Assistant Professor Department of Computer Science San Diego State University 5500 Campanile Dr., San Diego, CA, 92115 Phone: (619) 594-4302 Email: [email protected]

* Corresponding Author

Length of Paper: 3423 words, 1 table and 7 figures @ 250 words each, 5,423 equivalent words Submitted for presentation and publication to the 94th annual meeting of the Transportation Research Board (TRB) January 11-15, 2015, Washington, DC

ABSTRACT Smartphones and other portable personal devices that integrate global positioning systems (GPS), Bluetooth Low Energy (BLE), and advanced computing technologies have become more accessible due to affordable price, product innovation, and people’s desire to be connected. As more people own these devices, there are greater opportunities for data acquisition in Intelligent Transportation Systems (ITS), and for vehicle-to-infrastructure (V2I) communications. This study proposes a method of warning drivers of horizontal curves in order to prevent motor vehicles from running off the road. The prototype system is a smartphone-based application equipped with BLE technology and a head-up display (HUD). The system can track driver speed and compare vehicle position with curve locations in a real-time fashion. Messages can be wirelessly communicated from the smartphone to a receiving unit through BLE technology, and then displayed by HUD on the vehicle’s front windshield. In this way, drivers can receive alerts without looking away from their usual line of sight. System architecture, algorithm, and specific hardware are presented in detail. A field evaluation of the system is provided, and the overall results indicate relatively high accuracy.

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INTRODUCTION All components of smart transportation infrastructure need to be connected, monitored, and automated in order for the system to work effectively and efficiently. Conventional one-way communication between highway infrastructure and motorists through traffic control devices (TCDs) consists of pavement markings, traffic signs, and signals cautioning drivers about changes in lane configuration, geometric characteristics, and right-of-way priority. A variety of TCDs are installed at horizontal curves to warn drivers of turning direction and reducing speed. The TCDs are not adapted to the driver’s position and speed; it is expected that drivers notice these warnings. However, the ability of a driver to see a TCD can be compromised by inclement weather, low light conditions, vandalized signs or missing signs. One-way communication partially contributes to a disproportionally high number of fatal crashes involving roadway horizontal curves. According to the Fatality Analysis Reporting System (FARS), 25 % of fatal crashes occurred along horizontal curves on the U.S. two-lane rural highways in 2006 (1), and 90.2 % of fatal single-vehicle crashes that occurred on these curved roads were run-off-road (ROR) crashes (2). Despite being recognized as one of the top safety improvement focus areas by many state Departments of Transportation (DOTs), horizontal curve-related crashes occur at an alarming rate. Many of these crashes can be attributed to human errors such as inattentiveness, recklessness, distraction, and driving under the influence. Human errors, however, can potentially be avoided if drivers receive more advanced warnings. More active and effective communication between vehicle operators and roadway infrastructure can help to mitigate collisions. The popularity of smartphones that are equipped with global positioning systems (GPS) has prompted exponential growth of providing location-specific services and information. According to the eMarketer website report (3), the growth of smartphones continues to increase at a rapid pace, and the number of smartphone users is expected to reach 1.75 billion in 2014. Technological advances have empowered modern smartphones to effectively communicate not only between callers, but between on-board or roadside sensors through wireless communications. Swift development in wireless communications, GPS, cloud computing and enhanced computational power enable engineers and researchers to explore, develop, and deploy new applications in a wider field. As an emerging wireless technology, Bluetooth Low Energy (i.e. Bluetooth LE or BLE) gives a superior performance with regard to low power consumption and data throughput (4). The widespread use of BLE in mobile phones, laptops, automobiles, etc. has fueled novel applications in areas such as healthcare, fitness, security, home automation industries and intelligent transportation systems (ITS) (4-9). Smartphones with BLE technology are assumed to be an ideal choice for vehicle-to-infrastructure (V2I) communications. Ultimately, safe in-vehicle communications should minimize the motor vehicle operators’ work load and should not create an additional distraction. A head-up display (HUD), initially developed for military aviation, can be used to display information directly on the vehicle’s front windshield. The driver’s view and focus will not be altered, and therefore, distraction-associated risk can be reduced. The safety benefits of HUD are becoming increasingly available in automobiles and commercial navigation systems. The purpose of this paper is to offer a prototype of a smartphone application that tracks driver position, computes arrival time at an imminent hazard (e.g., sharp curve), and alerts drivers through HUD. The application would not only improve driver decision making, but reduce the need for the state departments of transportation to retrofit curves.

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RELATED WORK The goals of improving safety, comfort and efficiency of national transportation systems motivate further development of wireless communications in ITS. The U.S. Department of Transportation (USDOT) has promoted the development of Wireless Access in Vehicular Environments (WAVE) based on IEEE 802.11p and IEEE 1609.x. A WAVE system consists of two classes of devices that allow bidirectional vehicle-to-vehicle (V2V or V2I) communication: roadside units (RSUs) and onboard units (OBUs) (10). V2I communications are an emerging technology based on wireless network protocol. Vehicles equipped with intelligent systems such as collision warning systems (CWS) or lane-keeping assistance systems (LKAS) are designed for safety (11). To date, some V2I systems have been developed based on available wireless communication options such as Worldwide Interoperability for Microwave Access (WiMAX, IEEE 802.16), Wireless Fidelity (Wi-Fi, IEEE 802.11) and Dedicated Short-Range Communication (DSRC, IEEE 802.11p ) (12). These wireless communication technologies have been designed for traffic data acquisition and dissemination systems, work zones, intersection collision warning systems and incident detection (13-16). Designing ITS network highly concerns about functionality, performance, reliability and cost. However, given the limit of communication infrastructure in rural areas, the selection of suitable V2I communication alternative can be challenging. Typical V2I data are processed in the short range distance with wireless communication systems. BT technology standardized as IEEE 802.15 is widely used in both industrial and commercial environments. BLE is a new wireless technology developed by the Bluetooth Special Interest Group (SIG) for short-range communications, which use the low energy feature of the BT v4.0 specification for controlling and monitoring applications. In December of 2013, BLE was updated to Core Specification V4.1. A BLE product can collect data and run for months or years on a tiny battery (5; 17). Given the widespread use of BT technology, it is likely that BLE will be widely used in smartphones(18). Smartphones powered by BLE technologies are attracting more and more attention from transportation researchers and wireless service providers because of the traffic and travel information they can provide. Manzoni et al. (19) used smartphone and BT technologies to present an interaction system using a V2V, as well as a driver-to-infrastructure communication system, to improve motorcycle safety. The system interacts with drivers through an audio system and is remotely maintained and monitored through a web server and HTTP communications. Rodrigues et al. proposed a system architecture designed for connected vehicles using sensorembedded smartphones, BT, a GPS receiver and accelerometer to collect and process data in real-time (20). Similar smartphone system architecture can also be found for V2V and V2I applications(21). Drivers are constantly receiving information through in-vehicle sensors, which could become a distraction if not handled properly. Alerting drivers without creating an extra distraction is important for the driver’s safety. HUD projects information on the front vehicle windshield without asking the driver to take her/his eyes off the roadway. Due to its safe design, HUD has been gradually adopted in new vehicle models by manufacturers such as GM and BMW. Many HUD systems (22-24) and several smartphone applications(25; 26) have been designed or developed in recent years, and these driver-assist devices provide navigation information such as current vehicle position, speed, traffic sign, lane configuration, etc. However,

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these commercially available HUD technologies are proprietary, meaning they cannot be broadly used. SYSTEM ARCHITECTURE AND DESIGN We proposed a system architecture that is comprised of three modules: smartphone module, communication module and HUD module (see Figure 1). Together, the three modules can track driver position, compute arrival time at an imminent hazardous location, and send alerts through HUD. In Figure 2, the necessary hardware includes a smartphone (iOS), a BLE shield, an Arduino, an LED matrix, and accessories (e.g., power source and cables). The work flow is designed as follows: the smartphone application sends a command to the BLE shield; the BLE shield converts the command into digital signals and forwards them to the Arduino UNO motherboard (i.e. an embedded microprocessor). Controlled by Arduino, the LED matrix displays messages through properly connected electronic wires and pins. Eventually, the LED matrix projects a message on the windshield. A cigarette power inverter with a USB converts 12 volts of vehicle power to an alternating current (AC) for the entire system. The rest of this section presents the detailed design and functions of each module. Smartphone (Apple iPhone 4s or later)

BLE Shield BLE IEEE 802.15

WiFi/3G/4G

Circuit Pin Connection

Curve Database

Wire and Circuit Pin Connection

Direct Projection

Windshield Display

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Medium 16*32 RGB LED Matrix

FIGURE 1 System Architecture.

Embedded Computer Motherboard (Arduino UNO)

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Power Inverter

iPhone 4s

Arduino and BLE Shield LED Matrix

Breadboard

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FIGURE 2 Actual Hardware. Smartphone Module This module explains the communication that takes place between the smartphone and the Arduino. The smartphone is the core of the system because it integrates the GPS, compass, digital map databases, and BLE communication interface. The GPS and compass provide vehicle location and orientation updates. The BLE interface allows the smartphone application to scan, connect, and communicate with the BLE through a universally unique identifier (UUID) (see Figure 3). Horizontal curve information (e.g., GPS coordinates, geometry, and description) can be stored in a spatial database that uses Google Maps or Apple iOS Maps as a navigational reference. For the convenience of operation, a mobile application based on Apple iPhone Operating System 7 (iOS 7) was developed using the Xcode Version 5.0 Software Development Kit (SDK) for the iPhone 4s or later models.

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FIGURE 3 BLE Interface Screenshots in iPhone 4s.

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Wireless Communication Module The Wireless communication module consists of the Arduino and BLE board. Arduino (27) is an open-source electronics prototyping platform. The Arduino Uno, a microcontroller board based on the ATmega328, can control physical objects. Arduino codes were developed using Arduino IDE to receive data from the BLE and send operations to control whether LED lights are on or off. Arduino Uno does not have built-in IEEE 802.15 connectivity; thus, the BLE is added to connect and configure commands from the smartphone. We used BLE shield version 2 from RedBear product (28) which is designed to work with the Ardunio board. The BLE shield connects to Arduino via a serial port that provides IEEE 802.15 network connectivity. HUD Module As the preliminary HUD, a 16×32 RGB color LED matrix panel (29) was used. This product has the capacity to show a variety of colors and provides the library with open source codes and a wiring tutorial (30) for developers. Once the display command is received by the BLE shield and the Arduino board, a program written for Arduino will control the message by sending electronic signals to the LED matrix. As illustrated in Figure 4, the LED matrix has such a strong emitting light intensity that the image can be clearly projected onto the windshield. Additionally, to display normal curve images, this module implements a mirroring function in the Arduino Uno program. Figure 4 depicts several curve images.

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FIGURE 4 Inverted Curve Arrows.

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Data Structure

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In this system we split horizontal curve data into two types of nodes (see Figure 5): clockwise curve node and counterclockwise curve node, and their locations are designated to the coordinates of the PC (point of curvature at the beginning of curve) or the PT (point of tangency at the end of curve).

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SD

Counterclockwise Curve Node and Advisory Speed

Direction of Travel SD: Safe Distance

Clockwise Curve Node and Advisory Speed

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Figure 5 Horizontal Curve Scenario.

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Each node is structured as: Curve_node = {latitude, longitude, loc_desc, G, curve_type, curve_dir, V, SD} where latitude and longitude are the coordinates of PC or PT; Loc_desc is used to identify a curve node including roadway name , mileage , travel direction and city or county name , for example , a curve node located in the eastbound 421.41 miles of the highway 14 in Brookings, South Dakota, is described as “421.41 at Hwy 14 EB Brookings SD”.; G is the roadway grade; Curve _type defines the horizontal curve type including simple curve, compound curve, reversed curve and spiral curve.; curve_dir represents the curve direction in the travel orientation; V is the advisory speed at the curve; SD is the safe distance threshold. Drivers can receive multiple warning messages displayed through HUD when travelling through an area with many curves; hence, it is important that messages are displayed within the proper time-frame (e.g. not too early, not too late). According to the equations of motion that assume a constant acceleration, the advance distance it takes a driver to perceive, react, and decelerate to the advisory speed of a curve can be formulated in Equation 1.

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𝑆 = 1.47𝑉0 𝑡 +

𝑉02 −𝑉 2 𝑎 𝑔

30( ±𝐺)



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where: S is the minimum safe distance (feet); 𝑉0 is the vehicle operating speed on a straight roadway (mph); V is the advisory speed at the curve (mph); t is the driver perception-reaction time (seconds), typically 2.5 seconds for design; a is the vehicle deceleration rate (ft./s2), with the recommended maximum deceleration of 0.34g used by the American Association of State Highway Transportation Officials (AASHTO); g is the gravitational constant (32.2 ft./s2); and G is the roadway grade (+ for uphill, -for downhill) in decimal. An alert will be activated once the vehicle enters the distance threshold for an advance warning. For example, an advance warning threshold of 15 seconds for a vehicle traveling at 60 mph is approximately 0.25 miles. Various thresholds are provided for different types of curves (e.g. a curve radius of less than 1000 feet, a hidden curve without sufficient stop sight distance, etc.). Algorithm and Control Rules To detect a curve within the warning distance, the built-in GPS and compass in the smartphone identify a search radius based on the safe distance and compare it with vehicle location and travel direction. To determine the nearest curve, the smartphone module implements a two-step search algorithm as shown in Figure 6. Search Rule I involves obtaining curve data from the spatial database and identifying candidate curve locations. Search Rule II identifies the nearest curve location by distance, computes travel time, sends alert commands to HUD, and displays the proper warning. Once the vehicle enters the safe distance range, a correspondent warning is shown on the front windshield. Different signs are designed to effectively warn drivers. If the vehicle is approaching a speed below the curve’s posted speed limit, HUD displays a constant green curve arrow. If the vehicle is operating at a speed within 5 mph above the posted speed limit, the system blinks a red curve arrow once per second. When the vehicle is exceeding the curve’s posted speed limit by 5-10 mph, the red curve sign blinks faster at two times per second. Lastly, if the vehicle’s speed is 10 mph above the posted speed limit, the red curve sign blinks at a faster rate, or four times per second. The sign disappears when vehicle is past the beginning point of curve.

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FIGURE 6 iOS Mobile application Algorithm of Curve Warning. FIELD TEST AND RESULTS To evaluate system performance, Figure 7 shows a 30-mile test route on US Route 14 going from Brookings to Lake Preston in South Dakota. This route has seven horizontal curves or 14 curve nodes, meaning 14 warnings should be displayed during this journey. Vehicle’s speed was obtained by the iPhone 4s via 4G and via the speedometer. Curve warning messages were recorded during two round trips.

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FIGURE 7 Field Test Route. Evaluation of Speed Accuracy

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Vehicle speed plays an important role in this system; therefore, the accuracy of the speed obtained from the smartphone needs to be evaluated. During the field test, speed values from the iPhone 4s and from the vehicle’s speedometer are compared minute by minute. The absolute difference between two speed values is between 0 and 3 mph with a mean of 1.11 mph. Mean Absolute Percentage Error (MAPE) in Equation 2 is used to evaluate the speed deviation.

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where N is the number of samples, Sv denotes the speed value from vehicle’s speedometer and SGPS is the speed value obtained from GPS receiver on iPhone 4s. The MAPE results are approximately at 2%. Statistical analysis suggests that the speed precision obtained from the GPS receiver on the iPhone 4s is acceptable.

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𝑆𝑣 −𝑆𝐺𝑃𝑆

𝑀𝐴𝑃𝐸 = 𝑁 ∑𝑁 𝑛=0 |

𝑆𝑣

|

(2)

Assessment of Curve Detection Algorithm In this field test, the curve detection algorithm was verified. Table 1 shows that 85% of the curves were detected in the field test. Missing alerts can be attributed to a BLE connection error. The “Invalid” column refers to incorrect alerts, for example, it should be left turning warning, but right turning warning is displayed. Errors can be attributed to the smartphone’s built-in GPS which dynamically searches location information and identifies the nearest curve by distance. The GPS accuracy level varies among different environment which can lead to warning errors. Hence, the GPS inaccuracy can sometimes cause the wrong sequence of curves, especially when two curve locations are within 500 feet from one another. TABLE 1 Assessment Result of Curve Detection Algorithm Trip Journey Expected Alerts Valid Missed Invalid 7 5 1 1 JourneyAB Trip 1 7 6 0 1 JourneyBA 7 7 0 0 JourneyAB Trip 2 7 6 0 1 JourneyBA Total 28 24 1 3

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CONCLUSIONS AND FUTURE WORK

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The work outlined in this paper proposes a smartphone-based horizontal curve warning system using GPS, BLE technology, and HUD. In this system, a smartphone application uses a vehicle’s real-time speed and position to warn drivers of imminent horizontal curves. The warning is projected on the vehicle’s front windshield, therefore improving safety by not requiring drivers to look away from the road. The GPS-equipped smartphone can exchange location information in a dynamic, real-time fashion through a 3G/4G/WiFi network. Moreover, the curve warning system uses an economically affordable device (HUD) as well as open source wireless communications that can integrate, reconfigure, and customize various data sources (e.g., state DOT data sources). The system was tested via a 4G network on a selected highway route with seven curves where vehicle speed accuracy and curve detection accuracy were evaluated. It was determined

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that the speed values from the iPhone are within 2% of those on the vehicle’s speedometer. While a couple of curves were displayed in the wrong sequence due to their close proximity, curve detection results show that 85% of curves were successfully detected with this system. The system performance will be evaluated further under various scenarios and conditions, such as BLE data transmission, GPS accuracy in different conditions (e.g. with internet and without internet, mountainous road, high density road network in urban area) and algorithm accuracy in the future work. Human factors evaluation will also be studied to ensure that the use of this system will not introduce unintended safety problems. Other popular platform alternatives such as Android and Windows 8 can be explored. While this application is designed for highway horizontal curves, it can conveniently be applied to other areas such as work zones, highway-rail grade crossings, and wrong-way traffic as long as location-specific information is available.

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